def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=-100.0,
ignore_noanswer=False, ignore_allanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer,
ignore_allanswer=ignore_allanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
'''
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
'''
rec1_se =-10
rec1_svm = -10
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
#print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
if (rec1_se<-5):
rec1_se = p_se
rec1_svm = p_svm
'''
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
'''
print "Table view"
print " MRR MAP P@1"
print "REF_FILE %5.2f %5.2f %5.2f" % (mrr_se, map_se*100, rec1_se)
print "SVM %5.2f %5.2f %5.2f" % (mrr_svm, map_svm*100, rec1_svm)
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "acc\tf1\tMAP\tMRR\tAvgRec"
print "%.4f %4.4f %4.4f %4.4f %4.4f" % (acc, f1, map_svm, mrr_svm,
avg_acc1_svm)
def eval_search_engine(res_fname, format, th=10):
ir = read_res_file(res_fname, format)
# evaluate IR
rec = metrics.recall_of_1(ir, th)
acc = metrics.accuracy(ir, th)
acc1 = metrics.accuracy1(ir, th)
acc2 = metrics.accuracy2(ir, th)
mrr = metrics.mrr(ir, th)
print("%13s" % "IR")
print("MRRof1: %5.2f" % mrr)
for i, (r, a, a1, a2) in enumerate(zip(rec, acc, acc1, acc2), 1):
print(
"REC-1@%02d: %6.2f ACC@%02d: %6.2f AC1@%02d: %6.2f AC2@%02d: %4.0f"
% (i, r, i, a, i, a1, i, a2))
print()
print(
"REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
)
print(
"ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
)
print(
"AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
)
print("AC2 - the absolute number of correct answers at @X")
def eval_search_engine(res_fname, format, th=50): ir = read_res_file(res_fname, format) # evaluate IR rec = metrics.recall_of_1(ir, th) acc = metrics.accuracy(ir, th) acc1 = metrics.accuracy1(ir, th) acc2 = metrics.accuracy2(ir, th) mrr = metrics.mrr(ir, th) # MAP map_ir = metrics.map(ir) print "%10s" %"IR" print "MRR: %5.2f" % mrr print "MAP: %5.2f" % map_ir for i, (r, a, a1, a2) in enumerate(zip(rec, acc, acc1, acc2), 1): print "REC-1@%02d: %6.2f ACC@%02d: %6.2f AC1@%02d: %6.2f AC2@%02d: %4.0f" %(i, r, i, a, i, a1, i, a2) print print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)" print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions" print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)" print "AC2 - the absolute number of correct answers at @X"
def get_cv_evaluation_results(qid_aid_label_list, y_pred):
predictions_dict = get_cv_ranked_predictions_dict(qid_aid_label_list,
y_pred)
logging.debug("Num of questions: %d" % (len(predictions_dict)))
mrr_score = m.mrr(predictions_dict, 1000)
map_score = m.map(predictions_dict) * 100
p1_score = m.recall_of_1(predictions_dict, 1000)[0]
return mrr_score, map_score, p1_score
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
print
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
def get_evaluation_results(df,
y_pred,
skip_all_positives_and_all_negatives=True):
predictions_dict = get_ranked_predictions_dict(
df,
y_pred,
skip_all_positives_and_all_negatives=
skip_all_positives_and_all_negatives)
logging.debug("Num of questions: %d" % (len(predictions_dict)))
mrr_score = m.mrr(predictions_dict, 1000)
map_score = m.map(predictions_dict) * 100
p1_score = m.recall_of_1(predictions_dict, 1000)[0]
return mrr_score, map_score, p1_score
def stats_cv(path=".", format="trec", prefix="svm", th=50, verbose=False):
mrrs_se = []
mrrs_svm = []
abs_mrrs = []
rel_mrrs = []
maps_se = []
maps_svm = []
abs_maps = []
rel_maps = []
recalls1_se = []
recalls1_svm = []
abs_recalls = []
rel_recalls = []
oracle_mrrs = []
oracle_maps = []
oracle_recs1 = []
num_folds = 0
print "%13s %5s %7s %7s" %("IR", "SVM", "(abs)", "(rel)")
for fold in sorted(os.listdir(path)):
currentFold = os.path.join(path, fold)
if not os.path.isdir(currentFold):
continue
if not fold.startswith("fold"):
logging.warn("Directories containing CV folds should start with 'fold'")
continue
print fold
# Relevancy file
res_fname = os.path.join(currentFold, "%s.test.res" % prefix)
if not os.path.exists(res_fname):
logging.error("Relevancy file not found: %s", res_fname)
sys.exit(1)
# Predictions file
pred_fname = os.path.join(currentFold, "%s.pred" % prefix)
if not os.path.exists(pred_fname):
logging.error("SVM prediction file not found: %s", pred_fname)
sys.exit(1)
try:
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose)
except:
logging.error("Failed to process input files: %s %s", res_fname, pred_fname)
logging.error("Check that the input file format is correct")
sys.exit(1)
# MRR
mrr_se = metrics.mrr(ir, th) or 1
mrr_svm = metrics.mrr(svm, th)
mrrs_se.append(mrr_se)
mrrs_svm.append(mrr_svm)
# improvement
abs_mrr_diff = mrr_svm - mrr_se
rel_mrr_diff = (mrr_svm - mrr_se)*100/mrr_se
abs_mrrs.append(abs_mrr_diff)
rel_mrrs.append(rel_mrr_diff)
print "MRR: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (mrr_se, mrr_svm, abs_mrr_diff, rel_mrr_diff)
# MAP
map_se = metrics.map(ir) or 1
map_svm = metrics.map(svm)
maps_se.append(map_se)
maps_svm.append(map_svm)
# improvement
abs_map_diff = map_svm - map_se
rel_map_diff = (map_svm - map_se)*100/map_se
abs_maps.append(abs_map_diff)
rel_maps.append(rel_map_diff)
print "MAP: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (map_se, map_svm, abs_map_diff, rel_map_diff)
# Recall-of-1@1
rec_se = metrics.recall_of_1(ir, th)[0] or 1
rec_svm = metrics.recall_of_1(svm, th)[0]
recalls1_se.append(rec_se)
recalls1_svm.append(rec_svm)
# improvement
abs_rec_diff = rec_svm - rec_se
rel_rec_diff = (rec_svm - rec_se)*100/rec_se
abs_recalls.append(abs_rec_diff)
rel_recalls.append(rel_rec_diff)
print "P@1: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (rec_se, rec_svm, abs_rec_diff, rel_rec_diff)
num_folds += 1
'''
mrr_oracle = metrics.oracle_mrr(ir, th)
map_oracle = metrics.oracle_map(ir)
prec_oracle = metrics.oracle_precision(ir, th)[0]
rec1_oracle = metrics.oracle_recall_of_1(ir, th)[0]
oracle_mrrs.append(mrr_oracle)
oracle_maps.append(map_oracle)
oracle_recs1.append(rec1_oracle)
print "Oracle MRR: %5.2f, Oracle MAP: %5.2f, Oracle prec: %5.2f, Oracle rec@1: %5.2f" % (mrr_oracle, map_oracle, prec_oracle, rec1_oracle)
'''
# mrrs
avg_mrr_se, std_mrr_se = mean_and_std(mrrs_se)
avg_mrr_svm, std_mrr_svm = mean_and_std(mrrs_svm)
avg_abs_impr_mrr, std_abs_impr_mrr = mean_and_std(abs_mrrs)
avg_rel_impr_mrr, std_rel_impr_mrr = mean_and_std(rel_mrrs)
#oracle_avg_mrr, std_oracle_avg_mrr = mean_and_std(oracle_mrrs)
# maps
avg_map_se, std_map_se = mean_and_std(maps_se)
avg_map_svm, std_map_svm = mean_and_std(maps_svm)
avg_abs_impr_map, std_abs_impr_map = mean_and_std(abs_maps)
avg_rel_impr_map, std_rel_impr_map = mean_and_std(rel_maps)
#oracle_avg_map, std_oracle_avg_map = mean_and_std(oracle_maps)
# recall
avg_rec1_se, std_rec1_se = mean_and_std(recalls1_se) # se
avg_rec1_svm, std_rec1_svm = mean_and_std(recalls1_svm) # svm
avg_abs_impr_rec1, std_abs_impr_rec1 = mean_and_std(abs_recalls) # absolute
avg_rel_impr_rec1, std_rel_impr_rec1 = mean_and_std(rel_recalls) # relative
#oracle_avg_rec1, std_oracle_avg_rec1 = mean_and_std(oracle_recs1)
FMT = u"%3s: %5.2f \u00B1 %4.2f %5.2f \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f"
#ORACLE_FMT = u"Oracle MRR: %5.2f \u00B1 %4.2f, Oracle MAP: %5.2f \u00B1 %4.2f, Oracle P@1: %5.2f \u00B1 %4.2f"
print
print "Averaged over %s folds" % num_folds
print "%17s %12s %14s %14s" %("IR", "SVM", "(abs)", "(rel)")
print FMT % ("MRR", avg_mrr_se, std_mrr_se, avg_mrr_svm, std_mrr_svm, avg_abs_impr_mrr, std_abs_impr_mrr, avg_rel_impr_mrr, std_rel_impr_mrr)
print FMT % ("MAP", avg_map_se, std_map_se, avg_map_svm, std_map_svm, avg_abs_impr_map, std_abs_impr_map, avg_rel_impr_map, std_rel_impr_map)
print FMT % ("P@1", avg_rec1_se, std_rec1_se, avg_rec1_svm, std_rec1_svm, avg_abs_impr_rec1, std_abs_impr_rec1, avg_rel_impr_rec1, std_rel_impr_rec1)
def stats_cv(path=".",
format="trec",
prefix="svm",
th=50,
suf="",
verbose=False,
truth_file=None,
ignore_noanswer=False,
cut_truth_map_at_N=None):
mrrs_se = []
mrrs_svm = []
abs_mrrs = []
rel_mrrs = []
maps_se = []
maps_svm = []
abs_maps = []
rel_maps = []
recalls1_se = []
recalls1_svm = []
abs_recalls = []
rel_recalls = []
num_folds = 0
truth = read_truth_file(truth_file, format, cut_truth_map_at_N)
print "%13s %5s %7s %7s" % ("IR", "SVM", "(abs)", "(rel)")
for fold in sorted(os.listdir(path)):
currentFold = os.path.join(path, fold)
if not os.path.isdir(currentFold):
continue
if not fold.startswith("fold"):
logging.warn(
"Directories containing CV folds should start with 'fold'")
continue
print fold
# Relevancy file
res_fname = os.path.join(currentFold, "%s.relevancy" % prefix)
if not os.path.exists(res_fname):
logging.error("Relevancy file not found: %s", res_fname)
sys.exit(1)
# Predictions file
pred_fname = os.path.join(currentFold, "%s.pred" % (prefix + suf))
if not os.path.exists(pred_fname):
logging.error("SVM prediction file not found: %s", pred_fname)
sys.exit(1)
try:
ir, svm = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
ignore_noanswer=ignore_noanswer,
truth_map=truth)
except:
logging.error("Failed to process input files: %s %s", res_fname,
pred_fname)
logging.error("Check that the input file format is correct")
sys.exit(1)
# MRR
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
mrrs_se.append(mrr_se)
mrrs_svm.append(mrr_svm)
# improvement
abs_mrr_diff = mrr_svm - mrr_se
rel_mrr_diff = (mrr_svm - mrr_se) * 100 / mrr_se
abs_mrrs.append(abs_mrr_diff)
rel_mrrs.append(rel_mrr_diff)
print "MRR: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
mrr_se, mrr_svm, abs_mrr_diff, rel_mrr_diff)
# MAP
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
maps_se.append(map_se)
maps_svm.append(map_svm)
# improvement
abs_map_diff = map_svm - map_se
rel_map_diff = (map_svm - map_se) * 100 / map_se
abs_maps.append(abs_map_diff)
rel_maps.append(rel_map_diff)
print "MAP: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
map_se * 100, map_svm * 100, abs_map_diff, rel_map_diff)
# Recall-of-1@1
rec_se = metrics.recall_of_1(ir, th)[0]
rec_svm = metrics.recall_of_1(svm, th)[0]
recalls1_se.append(rec_se)
recalls1_svm.append(rec_svm)
# improvement
abs_rec_diff = rec_svm - rec_se
rel_rec_diff = (rec_svm - rec_se) * 100 / rec_se
abs_recalls.append(abs_rec_diff)
rel_recalls.append(rel_rec_diff)
print "P@1: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
rec_se, rec_svm, abs_rec_diff, rel_rec_diff)
num_folds += 1
# mrrs
avg_mrr_se, std_mrr_se = mean_and_std(mrrs_se)
avg_mrr_svm, std_mrr_svm = mean_and_std(mrrs_svm)
avg_abs_impr_mrr, std_abs_impr_mrr = mean_and_std(abs_mrrs)
avg_rel_impr_mrr, std_rel_impr_mrr = mean_and_std(rel_mrrs)
# maps
avg_map_se, std_map_se = mean_and_std(maps_se)
avg_map_svm, std_map_svm = mean_and_std(maps_svm)
avg_abs_impr_map, std_abs_impr_map = mean_and_std(abs_maps)
avg_rel_impr_map, std_rel_impr_map = mean_and_std(rel_maps)
# recall
avg_rec1_se, std_rec1_se = mean_and_std(recalls1_se) # se
avg_rec1_svm, std_rec1_svm = mean_and_std(recalls1_svm) # svm
avg_abs_impr_rec1, std_abs_impr_rec1 = mean_and_std(
abs_recalls) # absolute
avg_rel_impr_rec1, std_rel_impr_rec1 = mean_and_std(
rel_recalls) # relative
FMT = u"%3s: %5.2f \u00B1 %4.2f %5.2f \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f"
print
print "Averaged over %s folds" % num_folds
print "%17s %12s %14s %14s" % ("IR", "SVM", "(abs)", "(rel)")
print FMT % ("MRR", avg_mrr_se, std_mrr_se, avg_mrr_svm, std_mrr_svm,
avg_abs_impr_mrr, std_abs_impr_mrr, avg_rel_impr_mrr,
std_rel_impr_mrr)
print FMT % ("MAP", avg_map_se * 100, std_map_se, avg_map_svm * 100,
std_map_svm, avg_abs_impr_map, std_abs_impr_map,
avg_rel_impr_map, std_rel_impr_map)
print FMT % ("P@1", avg_rec1_se, std_rec1_se, avg_rec1_svm, std_rec1_svm,
avg_abs_impr_rec1, std_abs_impr_rec1, avg_rel_impr_rec1,
std_rel_impr_rec1)
print "Table view"
print " MRR MAP P@1"
print u"IR %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f" % (
avg_mrr_se, std_mrr_se, avg_map_se * 100, std_map_se * 100,
avg_rec1_se, std_rec1_se)
print u"SVM %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f" % (
avg_mrr_svm, std_mrr_svm, avg_map_svm * 100, std_map_svm * 100,
avg_rec1_svm, std_rec1_svm)
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print("")
print("*** Official score (MAP for SYS): %5.4f" % (map_svm))
print("")
print("")
print("******************************")
print("*** Classification results ***")
print("******************************")
print("")
print("Acc = %5.4f" % (acc))
print("P = %5.4f" % (p))
print("R = %5.4f" % (r))
print("F1 = %5.4f" % (f1))
print("")
print("")
print("********************************")
print("*** Detailed ranking results ***")
print("********************************")
print("")
print("IR -- Score for the output of the IR system (baseline).")
print("SYS -- Score for the output of the tested system.")
print("")
print("%13s %5s" % ("IR", "SYS"))
print("MAP : %5.4f %5.4f" % (map_se, map_svm))
print("AvgRec: %5.4f %5.4f" % (avg_acc1_ir, avg_acc1_svm))
print("MRR : %6.2f %6.2f" % (mrr_se, mrr_svm))
print("%16s %6s %14s %6s %14s %6s %12s %4s" %
("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2,
a_svm2) in enumerate(
zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1,
acc_se2, acc_svm2), 1):
print(
"REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f"
% (i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2,
a_svm2))
print()
print(
"REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)"
)
print(
"ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
)
print(
"AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
)
print("AC2 - the absolute number of correct answers at @X")
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
#print ""
#print "*** Official score (MAP for SYS): %5.4f" %(map_svm)
#print ""
#print ""
#print "******************************"
#print "*** Classification results ***"
#print "******************************"
#print ""
#print "Acc = %5.4f" %(acc)
#print "P = %5.4f" %(p)
#print "R = %5.4f" %(r)
#print "F1 = %5.4f" %(f1)
#print ""
#print ""
#print "********************************"
#print "*** Detailed ranking results ***"
#print "********************************"
#print ""
#print "IR -- Score for the output of the IR system (baseline)."
#print "SYS -- Score for the output of the tested system."
#print ""
#print "%13s %5s" %("IR", "SYS")
#print "MAP : %5.4f %5.4f" %(map_se, map_svm)
#print "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm)
#print "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm)
print "MAP : %5.4f\tMRR : %5.4f\tAvgRec: %5.4f" % (map_svm, mrr_svm,
avg_acc1_svm)
#print "Acc : %5.4f" %(acc)
#print "P : %5.4f" %(p)
#print "R : %5.4f" %(r)
#print "F1 : %5.4f" %(f1)
"""
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (conf_matrix['true']['true'] + conf_matrix['false']['false']) / (conf_matrix['true']['true'] + conf_matrix['false']['false'] + conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print ("")
print ("*** Official score (MAP for SYS): %5.4f" %(map_svm))
print ("")
print ("")
print( "******************************")
print( "*** Classification results ***")
print( "******************************")
print( "")
print( "Acc = %5.4f" %(acc))
print( "P = %5.4f" %(p))
print( "R = %5.4f" %(r))
print( "F1 = %5.4f" %(f1))
print( "")
print( "")
print( "********************************")
print( "*** Detailed ranking results ***")
print( "********************************")
print( "")
print( "IR -- Score for the output of the IR system (baseline).")
print( "SYS -- Score for the output of the tested system.")
print( "")
print( "%13s %5s" %("IR", "SYS"))
print( "MAP : %5.4f %5.4f" %(map_se, map_svm))
print( "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm))
print( "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm))
print( "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print( "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2))
print( "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)")
print( "ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions")
print( "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)")
print( "AC2 - the absolute number of correct answers at @X")
return map_svm